Indian Railways Machine Operator Interview Questions and Answers

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Logical, Verbal, reasoning 90 mins

Q1. ML algorithms and Explain it?
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Q2. Print even numbers in for loop?
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A discussion involving a number of people who are connected by some shared activity, interest or quality

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Machine learning - Code K-Means

Machine Learning - Code Neural Network

Q1. Machine Learning Fundamentals
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Q2. Python Advanced
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Q1. Why this company
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Q1. Factorial program?
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A factorial program is a program that calculates the factorial of a given number.
• Factorial of a number is the product of all positive integers up to that number.

• For example, the factorial of 5 (5!) is 5 x 4 x 3 x 2 x 1 = 120.

• Factorial program can be implemented using loops or recursion in programming languages like C, Java, Python, etc.
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Q2. Palindrome code?
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Q3. Check whether the number is prime or not?
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A prime number is a number greater than 1 that has no positive divisors other than 1 and itself.
• Check if the number is greater than 1.

• Loop through numbers from 2 to the square root of the number and check if any of them divides the number evenly.

• If no divisor is found, the number is prime.
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Q4. Check whether the number is even or not?
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To check if a number is even, divide it by 2 and see if there is a remainder.
• Divide the number by 2

• If there is no remainder, the number is even

• Example: 6 divided by 2 equals 3 with no remainder, so 6 is even
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Q1. What are the projects that you have worked related to Machine Learning ?
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I have worked on projects related to image recognition, natural language processing, and predictive analytics using machine learning.
• Developed a deep learning model for image recognition using convolutional neural networks

• Implemented a sentiment analysis system using natural language processing techniques

• Built a predictive analytics model for customer churn prediction in a telecom company
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Q1. Difference between bias and variance
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Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more
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Q2. What’s is Learning rate
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Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more
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